pacman::p_load(ggiraph, plotly, patchwork, DT, tidyverse) Hands-on Exercise 3
3 Programming Interactive Data Visualisation with R
3.2 Getting Started
3.3 Importing Data
exam_data <- read_csv("data/Exam_data.csv")Rows: 322 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): ID, CLASS, GENDER, RACE
dbl (3): ENGLISH, MATHS, SCIENCE
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.3.4 Interactive Data Visualisation - ggiraph methods
3.4.1 Tooltip effect with tooltip aesthetic
p <- ggplot(data = exam_data, # 使用考試資料
aes(x = MATHS)) + # X 軸為數學成績
geom_dotplot_interactive( # 繪製可互動的點狀分布圖
aes(tooltip = ID), # 滑鼠移到點上時顯示學生 ID
stackgroups = TRUE, # 將同一分組內的點進行堆疊
binwidth = 1, # 每 1 分為一個分箱區間
method = "histodot") + # 使用直方圖式的點狀堆疊方法
scale_y_continuous(NULL, # 不顯示 Y 軸標題
breaks = NULL) # 不顯示 Y 軸刻度
girafe(
ggobj = p, # 指定要轉為互動圖的 ggplot 物件
width_svg = 6, # 設定輸出圖形寬度
height_svg = 6 * 0.618 # 設定輸出圖形高度
)3.5 Interactivity 互動性
將滑鼠移到datapoint會出現student id ## 3.5.1 Displaying multiple information on tooltip
exam_data$tooltip <- c(paste0( # 新增一個欄位 tooltip，用來放滑鼠提示文字
"Name = ", exam_data$ID, # 第一行顯示 ID（你這裡 label 寫 Name，但實際放的是 ID）
"\n Class = ", exam_data$CLASS)) # 換行後顯示 CLASS（\n 代表換行）；c() 這裡其實可省略
p <- ggplot(data = exam_data, # 指定資料來源為 exam_data
aes(x = MATHS)) + # 設定 x 軸使用數學成績 MATHS
geom_dotplot_interactive( # 畫「可互動」的 dotplot（點狀直方圖）
aes(tooltip = exam_data$tooltip), # 滑鼠移到點上顯示 tooltip 欄位內容（建議寫 tooltip 而不是 exam_data$tooltip）
stackgroups = TRUE, # 同一個分箱內的點會往上堆疊
binwidth = 1, # 每 1 分作為一個分箱區間
method = "histodot") + # 用直方圖式的方式分箱並堆疊點
scale_y_continuous(NULL, # 不顯示 y 軸標題
breaks = NULL) # 不顯示 y 軸刻度（dotplot 的 y 只是堆疊高度，用不到）
girafe( # 將 ggplot 物件轉成可互動的 html widget
ggobj = p, # 指定要互動化的圖是 p
width_svg = 8, # 輸出 SVG 的寬度
height_svg = 8 * 0.618 # 輸出 SVG 的高度（用黃金比例設定視覺比例）
)3.6 Interactivity
將滑鼠移到點上，出現student ID 及 Class
3.6.1 Customising Tooltip style
tooltip_css <- "background-color:white; #<< # 建立一段 CSS 字串，用來自訂 tooltip（提示框）的外觀
font-style:bold; color:black;" #<< # 設定字體樣式與文字顏色；注意：bold 通常應該用 font-weight:bold
p <- ggplot(data = exam_data, # 指定資料來源為 exam_data
aes(x = MATHS)) + # X 軸為數學成績 MATHS
geom_dotplot_interactive( # 繪製可互動的 dotplot（點狀直方圖）
aes(tooltip = ID), # 滑鼠移到點上時顯示 ID 作為 tooltip 內容
stackgroups = TRUE, # 同一個分箱中的點會往上堆疊
binwidth = 1, # 每 1 分為一個分箱（bin）
method = "histodot") + # 使用 histodot 方法：先分箱再用點堆疊出分布
scale_y_continuous(NULL, # 不顯示 Y 軸標題
breaks = NULL) # 不顯示 Y 軸刻度（y 只是堆疊高度用）
girafe( # 將 ggplot 物件轉成可互動的 html widget
ggobj = p, # 指定要互動化的 ggplot 物件為 p
width_svg = 6, # 設定輸出 SVG 的寬度
height_svg = 6 * 0.618, # 設定輸出 SVG 的高度（黃金比例）
options = list( #<< # 設定 girafe 的互動選項（以 list 傳入多個 opts_*）
opts_tooltip( #<< # 設定 tooltip（滑鼠提示框）的顯示方式
css = tooltip_css)) #<< # 套用上面自訂的 CSS 字串到 tooltip
) # 結束 girafe 呼叫3.6.2 Displaying statistics on tooltip
tooltip <- function(y, ymax, accuracy = .01) {
mean <- scales::number(y, accuracy = accuracy)
sem <- scales::number(ymax - y, accuracy = accuracy)
paste("Mean maths scores:", mean, "+/-", sem)
}
gg_point <- ggplot(data=exam_data,
aes(x = RACE),
) +
stat_summary(aes(y = MATHS,
tooltip = after_stat(
tooltip(y, ymax))),
fun.data = "mean_se",
geom = GeomInteractiveCol,
fill = "light blue"
) +
stat_summary(aes(y = MATHS),
fun.data = mean_se,
geom = "errorbar", width = 0.2, size = 0.2
)Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.girafe(ggobj = gg_point,
width_svg = 8,
height_svg = 8*0.618)3.6.3 Hover effect with data_id aesthetic
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(data_id = CLASS),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618
) 3.6.4 Styling hover effect
將highlight效果修改
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(data_id = CLASS),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618,
options = list(
opts_hover(css = "fill: #202020;"),
opts_hover_inv(css = "opacity:0.2;")
)
) 3.6.5 Combining tooltip and hover effect
highlight效果修改並且呈現資料
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(tooltip = CLASS,
data_id = CLASS),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618,
options = list(
opts_hover(css = "fill: #202020;"),
opts_hover_inv(css = "opacity:0.2;")
)
) 3.6.6 Click effect with onclick
點擊後連結到外部網站
exam_data$onclick <- sprintf("window.open(\"%s%s\")",
"https://www.moe.gov.sg/schoolfinder?journey=Primary%20school",
as.character(exam_data$ID))
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(onclick = onclick),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618) 3.6.7 Coordinated Multiple Views with ggiraph
組合多張圖表。滑鼠hover到左圖的一個點時，右圖也會出現對應的表
p1 <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(data_id = ID),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
coord_cartesian(xlim=c(0,100)) +
scale_y_continuous(NULL,
breaks = NULL)
p2 <- ggplot(data=exam_data,
aes(x = ENGLISH)) +
geom_dotplot_interactive(
aes(data_id = ID),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
coord_cartesian(xlim=c(0,100)) +
scale_y_continuous(NULL,
breaks = NULL)
girafe(code = print(p1 + p2),
width_svg = 6,
height_svg = 3,
options = list(
opts_hover(css = "fill: #202020;"),
opts_hover_inv(css = "opacity:0.2;")
)
) 3.7 Interactive Data Visualisation - plotly methods!
##3.7.1 Creating an interactive scatter plot: plot_ly() method
plot_ly(data = exam_data,
x = ~MATHS,
y = ~ENGLISH)No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plotly.com/r/reference/#scatterNo scatter mode specifed:
Setting the mode to markers
Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode3.7.2 Working with visual variable: plot_ly() method
依照種族進行顏色分類
plot_ly(data = exam_data,
x = ~ENGLISH,
y = ~MATHS,
color = ~RACE)No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plotly.com/r/reference/#scatterNo scatter mode specifed:
Setting the mode to markers
Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode3.7.3 Creating an interactive scatter plot: ggplotly() method
p <- ggplot(data=exam_data,
aes(x = MATHS,
y = ENGLISH)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
ggplotly(p)3.7.4 Coordinated Multiple Views with plotly
d <- highlight_key(exam_data)
p1 <- ggplot(data=d,
aes(x = MATHS,
y = ENGLISH)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
p2 <- ggplot(data=d,
aes(x = MATHS,
y = SCIENCE)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
subplot(ggplotly(p1),
ggplotly(p2))3.8 Interactive Data Visualisation - crosstalk methods
3.8.1 Interactive Data Table: DT package
DT::datatable(exam_data, class= "compact")3.8.2 Linked brushing: crosstalk method
d <- highlight_key(exam_data)
p <- ggplot(d,
aes(ENGLISH,
MATHS)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
gg <- highlight(ggplotly(p),
"plotly_selected")
crosstalk::bscols(gg,
DT::datatable(d),
widths = 5) Setting the `off` event (i.e., 'plotly_deselect') to match the `on` event (i.e., 'plotly_selected'). You can change this default via the `highlight()` function.4 Programming Animated Statistical Graphics with R
4.2 Getting Started
4.2.1 Loading the R packages
pacman::p_load(readxl, gifski, gapminder, plotly, gganimate, tidyverse)4.2.2 Importing the data
col <- c("Country", "Continent")
globalPop <- read_xls("data/GlobalPopulation.xls",
sheet="Data") %>%
mutate_at(col, as.factor) %>%
mutate(Year = as.integer(Year))4.3 Animated Data Visualisation: gganimate methods
4.3.1 Building a static population bubble plot
ggplot(globalPop, aes(x = Old, y = Young,
size = Population,
colour = Country)) +
geom_point(alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(title = 'Year: {frame_time}',
x = '% Aged',
y = '% Young') 
4.3.2 Building the animated bubble plot
ggplot(globalPop, aes(x = Old, y = Young,
size = Population,
colour = Country)) +
geom_point(alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(title = 'Year: {frame_time}',
x = '% Aged',
y = '% Young') +
transition_time(Year) +
ease_aes('linear') 
4.4 Animated Data Visualisation: plotly
4.4.1 Building an animated bubble plot: ggplotly() method
gg <- ggplot(globalPop,
aes(x = Old,
y = Young,
size = Population,
colour = Country)) +
geom_point(aes(size = Population,
frame = Year),
alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(x = '% Aged',
y = '% Young')Warning in geom_point(aes(size = Population, frame = Year), alpha = 0.7, :
Ignoring unknown aesthetics: frameggplotly(gg)Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data:
被替換的項目不是替換值長度的倍數gg <- ggplot(globalPop,
aes(x = Old,
y = Young,
size = Population,
colour = Country)) +
geom_point(aes(size = Population,
frame = Year),
alpha = 0.7) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(x = '% Aged',
y = '% Young') +
theme(legend.position='none')Warning in geom_point(aes(size = Population, frame = Year), alpha = 0.7):
Ignoring unknown aesthetics: frameggplotly(gg)Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data:
被替換的項目不是替換值長度的倍數4.4.2 Building an animated bubble plot: plot_ly() method
bp <- globalPop %>%
plot_ly(x = ~Old,
y = ~Young,
size = ~Population,
color = ~Continent,
sizes = c(2, 100),
frame = ~Year,
text = ~Country,
hoverinfo = "text",
type = 'scatter',
mode = 'markers'
) %>%
layout(showlegend = FALSE)
bp